The term disproportionality relates to the matter of diversity in cultures and languages and still represents some of the most significant as well as intransigent problems in the fields of special education and behavioural studies (Skiba et al., 2005, p.130). Being a contentious topic, disproportionality describes students of color and special education needs thus revealing that biases exist in the selection of the variables of interest under the scope of a study. More significantly, there is a showcase lack of data for stronger conclusions (Artiles, 2010, p.291). The aim of this study is to highlight the two key challenges in examining the disproportionality of students of color from a special education perspective. The write-up will also try to describe the problem-solving strategy and its rationale for the two challenges outlined.
There have been established two main challenges related to the topic under discussion. The first issue is the use of non-representative state data instead of the data from individuals and district-level (Bollmer, et al., 2007, p.197). Most studies use small population sizes and never admit that such sizes are not sufficient to make comprehensive disproportionality conclusions (Bollmer et al., 2007, p.197). There was inefficiency in data usage in the studies that are based on disproportionality. Research requires historical data in order to find solutions for inequalities in offering special education (Artiles, 2010, p.286). The rationale for using such data is based on the fallacy of representativeness of state-based data instead of individually-generated data (Artiles, 2010, p.291). An example of a cross-sectional study rather than historical one is described in a single Midwestern State. With such a study lacking historical comparison, the conclusions drawn are biased, and the findings cannot be generalized (Skiba, et al., 2005, p.132). Besides, the use of district-level data has surfaced prominently the reviewed articles, thus leading to the conclusion that the challenge in examining the variable disproportionality lies in the strength of the used data.
Second, most researchers over-rely on certain explanatory variables such as poverty, ethnicity/race among others, to predict the outcome of their research (Skiba, et al., 2005, p.133). The rationale to include more than explanatory variables was to develop a wide array of methodological and theoretical approaches that could integrate several professional processes. Most of the researchers do not base their arguments on the historicity of cultural and ethical factors (Skiba, et al., 2005, p.133).
The most important example was a bigger sample of white students as a comparison group because they were considered a majority group in terms of race or ethnicity in the selected studies (Bollmer, et al., 2007, p.197). In behavioral studies, discriminatory behaviors are compared with the practices that elevate respect to white individuals. This can lead to the appearance of bias and expectations regarding certain behavioral issues when research is being conducted (Skiba, et al., 2005, p.132). One strategy that has been employed is to develop a theoretically sound use of culture and its perspective to analyze the issue of disproportionality (Artiles et al., 2010, p.281). The use of strategic approaches that are employed in the articles such as the development of whole round study like proposition of the modification of risk ratio analysis can revolutionize the studies on disproportionality (Bollmer, et al., 2007, p.187).
To conclude, two key challenges in examining disproportionality as it pertains to the students of color and in special education sector were identified. In this analysis, the first challenge was the use of non-representative state data instead of the data from individuals. The second challenge was over-reliance on explanatory variables such as poverty, ethnicity, or race to predict the outcome of research in this topic. The studies reveal that when examining the disproportional nature of the students of color in special education, a keen focus needs to be placed on addressing data gaps as well as using acceptable variables such as cultural diversity instead of relying on previously used non-standardized variables.